In this election, we are hearing a lot about the relation between Big Business and the Federal Government. I wanted to see if the statistics actually backed up the claims that there is a connection between the two of them.
(This is great you're really thinking about entering real-world debates, awesome. You're going to get there. For now, though, does comparing the distributions or even the correlation between public opinion actually say anything about the relation between big business and the federal government? I'm not sure...)
The table above shows some basic descriptive statistics for the two feeling thermometers I was examining. We can see that the numbers for each are almost identical. The average, or mean, feeling for both Big Business and the Federal Government are quite close to the same. The standard deviation, or the average distance from the mean, are within one number from each other. These numbers show that there is certainly some connection between the two variables, but more analysis was needed. Figure 1 below shows a visual representation of the data that I have laid out above.
Fig. 1- Visual Representation of the Similarities Between the Big Business and Federal Government
The next thing I did was create a scatter plot. Scatter plots show the relationship between two variables. In Figure 2 below, you can see that as the feelings for the Federal Government go up, so do the feelings for Big Business. It can then be inferred that as the feelings for one decrease, so do the feelings toward the other. To do a little actual math to corroborate the findings from the scatter plot, I used R to find the actual correlation between the two variables. On a scale of -1.0 to +1.0 (where -1.0 is a completely negative correlation and +1.0 is a completely positive correlation), the number I came up with is 0.418, which seems like it could be a definitely nod to a positive correlation which backs up the information shown in both the scatter plot and what I learned from the descriptive statistics shown above: as feelings for one go up, so do feelings for the other, and vice-versa.
Fig. 2- Scatterplot Showing the Relationship Between the Variables
Destiny, this was a very efficient and nicely narrated exploration of the data. I really appreciate that you are trying to tell a story with the data, that's exactly what we want to do. Great. Just go back to the drawing board and think a little longer and harder about how you can zero in on your claims here, you want to be as rigorous as possible in making claims that your evidence directly confirms--rather than making big claims that your evidence only maybe vaguely confirms, you know? But this is an exciting start.
In this election, we are hearing a lot about the relation between Big Business and the Federal Government. I wanted to see if the statistics actually backed up the claims that there is a connection between the two of them.
(This is great you're really thinking about entering real-world debates, awesome. You're going to get there. For now, though, does comparing the distributions or even the correlation between public opinion actually say anything about the relation between big business and the federal government? I'm not sure...)The table above shows some basic descriptive statistics for the two feeling thermometers I was examining. We can see that the numbers for each are almost identical. The average, or mean, feeling for both Big Business and the Federal Government are quite close to the same. The standard deviation, or the average distance from the mean, are within one number from each other. These numbers show that there is certainly some connection between the two variables, but more analysis was needed. Figure 1 below shows a visual representation of the data that I have laid out above.
The next thing I did was create a scatter plot. Scatter plots show the relationship between two variables. In Figure 2 below, you can see that as the feelings for the Federal Government go up, so do the feelings for Big Business. It can then be inferred that as the feelings for one decrease, so do the feelings toward the other. To do a little actual math to corroborate the findings from the scatter plot, I used R to find the actual correlation between the two variables. On a scale of -1.0 to +1.0 (where -1.0 is a completely negative correlation and +1.0 is a completely positive correlation), the number I came up with is 0.418, which seems like it could be a definitely nod to a positive correlation which backs up the information shown in both the scatter plot and what I learned from the descriptive statistics shown above: as feelings for one go up, so do feelings for the other, and vice-versa.
Destiny, this was a very efficient and nicely narrated exploration of the data. I really appreciate that you are trying to tell a story with the data, that's exactly what we want to do. Great. Just go back to the drawing board and think a little longer and harder about how you can zero in on your claims here, you want to be as rigorous as possible in making claims that your evidence directly confirms--rather than making big claims that your evidence only maybe vaguely confirms, you know? But this is an exciting start.